The development of benchmark event data (ED) that employs comprehensive intentional feature characteristics and their connections to metrics supports process miners to evaluate methods more efficiently and reliably. However, the availability of diverse data often presents a challenge, ICPM 2024 Tool Demonstration Track, October 14-18, 2024, Kongens Lyngby, Denmark * Corresponding author. Envelope maldonado@dbs.ifi.lmu.de (A. Maldonado); anirudhsai027@gmail.com (S. A. Aryasomayajula); christian.frey@utn.de (C. M. M. Frey); seidl@dbs.ifi.lmu.de (T. Seidl) Orcid 0009-0009-8978-502X (A. Maldonado); 0000-0003-2458-6651 (C. M. M. Frey); 0000-0002-4861-1412 (T. Seidl) limiting the ability to thoroughly evaluate these novel methods. [1,2] Existing tools, such as PURPLE [3] and Declare4Py [4] [5], assist in generating event logs based on specific properties, but they are often constrained to basic features like trace length and the number of variants. To address this gap, we introduce an interactive online tool that integrates GEDI (Generating Event Data with Intentional Features) [6] -a framework that offers a broad range of properties, from statistical measures to entropy-based characteristics.

Our tool, interactive Generating Event Data with Intentional Features (iGEDI) empowers users to create event data tailored to their specific needs and objectives. By supporting seamless customization and integration, our innovative platform not only enhances the efficiency of testing process mining methods but also enables researchers to explore deeper connections between event data characteristics and evaluation metrics. In academic research, it's crucial to train and evaluate methods on diverse datasets to improve robustness and generalization. [6] demonstrates that evaluation metrics in process discovery are interrelated when models are trained on real-world benchmarks versus enriched data settings. Our tool also allows testing on synthetic datasets that mimic characteristics of inaccessible test data, such as those restricted by GDPR.

iGEDI, in fig. 1, is an interactive web application available both as an online service 1 and as a locally executable program 2 . It allows users to create configuration files to subsequently generate event data based on the framework "Generating Event Data with Intentional Features" (GEDI) [6]. GEDI employs (meta-)features, which numerically describe event log properties, to generate ED that have specific desired values. Supported event data features are presented in the Feature Extraction From Event Data (FEEED) [7] framework and include statistics as well as more complex, relationships between ED elements. Specifically, the feature types, that are currently supported by iGEDI concern simple summary statistics, entropies [8], and epa-based [9] about cardinality of traces/variants, trace length, variants, and (start/end) activities. For detailed feature descriptions and default settings for realistic bounds, we refer to our repository 3 .

Defined feature values are handled as targets in a hyperparameter optimization (HPO) problem. As proposed in [6], GEDI embeds the Process Tree and Log Generator (PTLG) proposed by Joucke et al. [2] and iteratively generates a process to optimize the parameters of PTLG, such that novel EDs' features align with the intended feature values, i.e. targets. The parameters of the embedded generator module are optimized by Bayesian Optimization (BO). Intuitively, BO iteratively selects and evaluates promising parameters, aiming to minimize an objective function. Formally, GEDI's objective function tackles a minimization problem of distances in feature space between an array of desired feature values and an array of generated ED's feature values. Hence, by leveraging GEDI, users can reproduce single event logs based on their desired feature values, or examine a grid of event logs, by regarding a hyperrectangle (grid) of specific feature value combinations.

Alongside implementing our architecture, iGEDI assists users throughout the specification process, automatically generates configuration files defining the feature space, and enables them to deploy GEDI either locally or as an interactive web application. Using the online web app, the user can directly download the generated event logs.

Next, we describe iGEDI's two options to create one or multiple event logs at once: log rmcv ense BPIC15f4 0.003 0.604 RTFMP 0.376 0.112 HD 0.517 0.254 Table 1: feature values for three real ED iGEDI supports manual input as well as input from a file. The supported file formats include event logs with a '.xes' extension or '.csv' files. For the event log, users have the option to select features of interest, and the generated event log will be optimized to closely match the feature values of the event log. For the '.csv' option, the file should contain at least one feature column, according to FEEED's [7] features, a 'log' column containing the name of the target event log. Therefore, one row represents a desired feature combination. Table 1 shows a possible example for such a '.csv' file. It depicts the feature values for ratio most common variant (rmcv) and epa-based normalized sequence entropy (ense), as in [9], of three public available datasets 4 , namely BPIC15f4 [10], RTFMP [11] and HD [12]. While rmcv compares the frequency of the most common variant to the overall number of traces in an event-log, the intuition behind ense [9] is to measure the variability/predictability of sequences captured by the event-log, considering their prefixes. A low ense indicates a process, where most cases follow similar paths, and a high value indicates a complex or highly variable process with many different paths.

Moreover, independently of the input option, the user can choose to generate point targets or a multidimensional grid of targets lying within a finite hyperrectangle:

Point targets mode (as seen in fig. 1) aims to reproduce ED directly aiming at specified feature values. In manual mode, the user can define specific target values for each selected feature for one generation experiment. Manual input requires semantic knowledge about selected features to choose values in sensible feature ranges. In contrast, inputting a table ("From CSV" option) and choosing the point target option will generate one event log per row, targeting their respective feature values for listed features. To reproduce ED, listed in table 1 in terms of the two selected features, iGEDI will produce three sets of targets for ED generation: [{rmcv: 0.003, ense: 0.604}, {rmcv: 0.376, ense: 0.112}, {rmcv: 0.517, 0.254}] to reproduce BPIC15f4, RTFMP and HD, respectively. Using this option, we generated ED and measured their euclidean similarity to respective targets, as shown in fig. 2. For the grid-based targets mode, iGEDI provides two possibilities to define the grid: the combinatorial method and the range method. Selecting the combinatorial option, the user can manually define how many combinations of features and feature values they want to generate ED for. By defining 𝑚 features with 𝑘 values, the user will get 𝑘 𝑚 combinations, where each combination represents an event log to be generated. Otherwise using an input csv table, iGEDI suggests 𝑓 𝑚𝑖𝑛 , 𝑓 𝑚𝑎𝑥 i.a. for each feature 𝑓 to create combinations of those feature values. In that case, e.g., table 1 results in statistical values as {rmcv 𝑚𝑖𝑛 : 0.003, rmcv 𝑚𝑎𝑥 : 0.517, ense 𝑚𝑖𝑛 : 0.112, ense 𝑚𝑎𝑥 : 0.604} and four generation experiments with targets: [{rmcv: 0.003, ense: 0.112}, {rmcv: 0.003, ense: 0.604}, {rmcv: 0.517, ense: 0.112}, {rmcv: 0.517, ense: 0.604}].

Finally, following the range option and manual input for each feature, the user can define a range [𝑓 𝑚𝑖𝑛 , 𝑓 𝑚𝑎𝑥 ] and a 𝑓 𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒 for each feature 𝑓. A step size defines a feature-specific sampling rate resulting in several samples along each feature dimension, which are separately used as target values. For example, if the user creates a grid of chosen features rmcv and ense from 0.0 to 1.0, with step size 0.1, GEDI will run 11 ⋅ 11 = 121 generation experiments, with varying feature combinations within those ranges. If the input is a table as table 1, value-based 𝑓 𝑚𝑖𝑛 and 𝑓 𝑚𝑎𝑥 are suggested based on the table's statistics, e.g., {rmcv 𝑚𝑖𝑛 : 0.003, rmcv 𝑚𝑎𝑥 : 0.517}.

After specifying desired options and feature values, users can run the experiments in our online application, and download the generated event logs. Alternatively, they can download the respective configuration file to run the generation locally, using the displayed command.

The quality of generated logs by GEDI in terms of feasibility, representativeness, and usage for benchmarking process mining tasks has been elaborately evaluated in [6]. In [6], an in-depth analysis of inter-feature relations in a generated grid setting is discussed. Figure 3 depicts the target distance between generated event logs and their respective targets with a scale. It contains 121 combinations of features created by the range option, where both features vary between 0.0 and 1.0 with a step size of 0.1, as in the example presented above. The lighter (darker) the color, the closer (further away) the measured feature values from the generated ED to its respective targets. The combination of rmcv and ense exemplarily shows a bright bottom left side and a dark top corner. By definition, a high value of rmcv indicates that the most common variant is highly frequent in the event log, which results in a high amount of cases following a similar path, represented by a low ense value. In contrast, a high ense value indicates high variability in the event logs paths, which constraints the most common path to a low frequency, resulting in low rmcv values. For this reason combinations of simultaneously high values for both rmcv and ense are unfeasible, as depicted in fig. 3. Therefore, the target distance of generated features indicates the level of feasibility for that particular feature value combination. Subsequently, further analysis about relations between feature values and metrics for a specific task as, e.g. process discovery, can be performed by benchmarking on highly feasible logs from the generated ED collection. Overall, our tool iGEDI enhances existing log generation tools by offering improved functionality and expanded features. It facilitates understanding the relationship between feature sets and evaluation metrics, aiding in the creation of tailored methods for specific tasks. It also supports model pretraining on diverse datasets, enhancing generalization. For testing, iGEDI can replicate feature-based behavior of real-world data, enabling reproducible benchmarking and exploration of feature-metric relations. However, the framework's effectiveness is sensitive to feature selection, with increased complexity potentially leading to unfeasible solutions during hyperparameter optimization.